Research Paper No. 1883
In Defense of Consciousness:
The Role of Conscious and Unconscious
Inputs in Consumer Choice
Itamar Simonson
February 2005
RESEARCH PAPER SERIES
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ABSTRACT
Although the argument that unconscious inputs are often key determinants of consumer decision
making is compelling, it may be overstated, particularly with respect to consumer choice. A
comparison of the role of conscious inputs (e.g., the attributes of options in the choice set) and
unconscious inputs (e.g., a seemingly irrelevant observation or task) indicates that the former
have a significant advantage. In particular, the impact of conscious inputs is supported by choice
task norms and is less susceptible to being lost in the “noise” that is characteristic of most natural
consumer environments (e.g., stores). Indeed, although consumers often have limited insight
into influences and processes producing their choices, the assumption that consumers base their
choices on conscious, willful evaluation of task-relevant inputs has been quite successful in
explaining a wide range of phenomena. It is expected that future research will put greater
emphasis on the interactions between conscious and unconscious influences.
Graduate School of Business, Stanford University, Stanford, CA 94305-5015;
Email: [email protected].
This commentary has benefited from the suggestions of Grainne Fitzsimons, Dale Griffin, Ran
Kivetz, John Lynch, Stephen Nowlis, Lee Ross, Baba Shiv, Christian Wheeler, and the JCP
Associate Editor.
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Building on a great deal of recent research, Dijksterhuis, Smith, van Baaren, and Wigboldus
(2005) observe that “many choices are made unconsciously and are strongly affected by the
environment” (Abstract). This conclusion is based on evidence concerning the link between
perception and behavior, particularly the role of mimicry and activation of stereotypes, and on
evidence regarding automatic goal pursuit. The notion that unconscious factors often have a
significant effect on consumer choice and that such influences have been under-researched until
recently is compelling. Furthermore, many of the studies that support the role of unconscious
influences on judgment and behavior are noteworthy in their elegance and ingenuity, often
demonstrating rather surprising effects. Thus, the article by Dijksterhuis and his colleagues (see
also Bargh 2002) is likely to make an important contribution by raising consumer researchers’
awareness of the importance of focusing more attention on the ways in which unconscious,
automatic processes might influence consumer decision making.
The conclusion that many psychological phenomena are largely determined by automatic,
unconscious processes and inputs has received a great deal of attention in the past 15 years or so,
though researchers have emphasized different aspects of such automatic, unconscious, intuitive
effects (e.g., Bargh 1997; Chaiken and Trope 1999; Epstein 1994; Frederick 2002; Kahneman
2003; Sloman 1996; Slovic et al. 2002). Integrating prior work, Kahneman (2003; see also
Kahneman and Frederick 2002) distinguishes between the operations of System 1 that tend to be
automatic, effortless, associative, implicit, and often emotionally charged, and operations of
System 2 that are slower, consciously and deliberately monitored, and potentially rule governed.
One of the conclusions that is implicitly or explicitly drawn from these related streams of
research, including the work reviewed in the Dijksterhuis et al. article, is that conscious
processes and consciously considered inputs play a relatively minor role in many, perhaps most,
judgments, choices, and behaviors. For example, Bargh asserts that “… everything that one
encounters is preconsciously screened and classified as either good or bad, within a fraction of a
second after encountering it” (1997, p. 23). Loewenstein recently argued that “consciousness
seems mainly to make sense of behavior after it is executed” (2001, p. 503). Dijksterhuis (2004)
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argues that unconscious thought tends to improve the quality of decisions. Such notions go well
beyond the well-established observation that decision makers often have limited insight into the
determinants of their own judgments and decisions (e.g., Nisbett and Wilson 1977).
Furthermore, in making the distinction between conscious and unconscious influences,
researchers have tended to paint the traditional emphasis on conscious inputs to decision making
with a broad brush. Thus, critics of conscious decision research often point to studies that
assumed that decisions are based on (cognitive) evaluation of the various options’ attributes or
on a detailed listing of the options’ pros and cons, as if conscious processing of perceived taskrelevant inputs implies a comprehensive evaluation of all aspects.
Importantly, the proposition that automatic, unconscious influences are the primary
drivers of decision making does not recognize significant distinctions within the very broad
category of judgment, decision making, and behavior. As discussed below, the notion that
automatic (System 1) influences are the default, with relatively infrequent override by conscious
(System 2) processes (e.g., Bargh 1997; Kahneman 2003), may fit many psychological
phenomena but does not adequately describe choice, where System 2 is usually the primary
influence. In particular, (a) consciously considered inputs tend to play a major role in choice
(including consumer choice), and (b) although understanding automatic, unconscious influences
on choice is certainly important, the many potential unconscious influences in typical consumer
choice environments (e.g., in stores) create high “noise” level and potential interactions that tend
to diminish the measurable significance of unconscious relative to conscious choice inputs.
Advantages of Conscious Influences on Consumer Choice
Dijksterhuis et al. motivate their discussion with an example of a shopper who finds himself at
the supermarket counter with 26 items in the cart but cannot remember how most of them got
there. Their explanation for the purchase of peanut butter, for example, is that “You hardly ever
buy peanut butter, but a small boy running through the aisles reminded you of your five-year old
nephew who loves peanut butter.” Thus, seeing a small boy running through the aisles made the
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need for peanut butter salient, leading the consumer to put a jar of peanut butter in the cart. This
example does not appear to depend primarily on unconscious influences and can be readily
explained as a conscious process. It is now well-accepted that consumers, and decision makers
more generally, often construct their preferences when they need to decide, which makes them
susceptible to a wide range of influences (for a review, see, e.g., Bettman, Luce, and Payne
1998). Thus, contrary to the classical economic view of people’s utility functions, it is no longer
assumed that tastes are generally stable and well-defined, and there is little doubt that the relative
salience of decision criteria depends, among others, on the consumer’s state of mind (see, e.g.,
Bettman and Sujan 1987; Wright and Kriewall 1980).
Returning to the peanut butter example, after seeing the boy, the mindless consumer
evidently visited the supermarket section where peanut butter options were displayed. In all
likelihood, given the lack of a compelling need for peanut butter, the decision to purchase peanut
butter and the act of placing it in the shopping cart involved at least some conscious processing
of the observed stimuli. For example, if the nephew’s favorite brand were not available, or if the
store’s price on that brand seemed unusually high, it would be quite possible that, the running
boy’s presence notwithstanding, peanut butter would not have been purchased. And even if the
decision to purchase peanut butter was instantly made upon observation of the child running in
the aisles, it appears highly unlikely that the consumer would randomly select a peanut butter.
Instead, the consumer would be likely to pay attention to one or more product attributes, such as
his nephew’s favorite brand, price, and fat content (given the sensitivity of the nephew’s parents
to that aspect). Overall, although the shopper would not recognize what triggered the peanut
butter idea, the choice would involve a set of mostly conscious processes.
More generally, typical consumer choice environments consist of the purchase options as
well as many other stimuli. The former have a great advantage with respect to attention and
impact on purchase decisions – they are the “main effects” that are usually perceived as relevant
to the decisions to be made. Conversely, other inputs in the consumer environment (e.g., in
stores) are usually not considered relevant, and their impact is more of an accident. Accidents
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and other low probability events do happen. For example, an observation of a hot dog or
something that is yellow may very well make a consumer more receptive to purchasing an extra
container of mustard. However, such effects, though clearly important to study and understand,
are likely to have a smaller impact on typical consumers' choices than the consciously considered
characteristics of the choice candidates and consumers’ beliefs about their preferences.
The accessibility-diagnosticity framework of Feldman and Lynch (1988) addresses
factors determining the likelihood that any cognition about an object will be used as an input to
decisions concerning that or a related object. This framework can be used to assess the impact of
conscious and unconscious inputs to consumer choice. According to this framework, the
likelihood that any cognition will be used as an input is a function of (1) the accessibility of the
input in memory, (2) the accessibility of alternative inputs, and (3) the diagnosticities of the input
and of alternative inputs. The role and meaning of accessibility have been discussed extensively,
though different definitions of accessibility have been applied (e.g., Higgins 1996; Kahneman
2003; Tulving & Pearlstone 1966). For example, Kahneman (2003, p. 699) conceptualized
accessibility broadly as determined by stimulus salience, selective attention, specific training,
associative activation, and priming. According to Lynch and Feldman (1988), an input is
diagnostic to the extent that consumers believe that the decision implied by that input alone
would accomplish their decision goals (e.g., maximizing utility, choosing a justifiable option).
A comparison between conscious inputs to choice, particularly the characteristics of
observed options, and unconscious inputs in the consumer environment indicates that the former
tend to have an overwhelming advantage on both the accessibility and diagnosticity dimensions.
When making choices, it is customary to consider the options and their characteristics and make
decisions accordingly. From a young age, children learn the ABC's of making choices and even
become adaptive decision makers (e.g., Gregan-Paxton and Roedder John 1995, 1997; Klayman
1985). Although the level of choice involvement is often low and consumers’ self-insight is
rather limited (e.g., Wilson and Schooler 1991), people generally believe that they should
consider the options’ characteristics when making decisions. This belief, in turn, implies that the
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characteristics of options tend to be more accessible and receive more attention than other, less
directly relevant inputs. Moreover, the observed characteristics of options are generally
perceived as diagnostic because people believe that they are the carriers of value and the proper
bases for choice.
By contrast, the main “impact advantage” of unconscious inputs is that they are
unconscious, making it unlikely that consumers would resist their influence. However, this
factor seems much less significant when compared with the overwhelming disadvantage of
unconscious inputs in terms of accessibility and perceived diagnosticity. Of course, consumers
do not seek unconscious inputs that might influence their decisions, and they do not consider
such inputs to be diagnostic or relevant. Indeed, had they been aware of the potential
unconscious effects on their behavior, consumers would have likely tried and succeeded in
eliminating them (though the degree of resistance to unconscious effects and the ability to
control them might vary).
Another significant impact disadvantage of unconscious inputs is their high susceptibility
to being lost in the noise that is characteristic of typical consumer (and many other) choice
environments (e.g., stores, on the Internet). Because, unlike options’ characteristics, potentially
influential unconscious inputs are not sought by consumers, their status in the information
processing hierarchy is not different from numerous other task-irrelevant inputs. For example, in
addition to seeing the running boy, the shopper described by Dijksterhuis et al. might have also
been exposed to an obese child, which could have negatively affected the likelihood of buying
peanut butter. Dijksterhuis et al. review work demonstrating the impact of (unconscious)
mimicry on behavior (e.g., van Baarn 2003). But in a typical store environment, there are many
people one might mimic. Can we predict or analyze with any accuracy such effects on consumer
choice?
Although a great deal of progress has been made in recent years in our understanding of
unconscious influences on judgment and decision making, there is little doubt that many other
such effects are yet to be uncovered in this relatively new research area. As Bargh (1997, p. 1))
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suggested, “it is hard to escape the forecast that as knowledge progresses regarding
psychological phenomena, there will be less of a role played by the free will or conscious choice
in accounting for them.” We might discover, for example, that certain colors and shapes trigger
particular responses and goals, and we may learn about various interactions among unconscious
stimuli. In the analysis of consumer choice, such new insights would likely enhance the noise
level, making it even more difficult to form predictions about the effects of unconscious inputs in
natural consumer choice environments. However, in an uncontrolled environment with many
potentially significant sources of unconscious influence, predicting the overall effect of such
inputs will be quite challenging.
The susceptibility of unconscious influences to being lost in the “noise” also has
implications for the manner in which studies of consumer choice are conducted. In particular,
whereas one can justify studying in isolation the impact of conscious inputs such as the
characteristics of options and sets, isolating unconscious influences, though often intriguing and
surprising, may not represent many real world effects. Consider, for example, the research
stream on context effects in choice (e.g., Huber, Payne, and Puto 1982; Simonson and Tversky
1992). Although these studies examined consumer response to very specific choice set
configurations, the focal inputs represented the types of options that consumers actually focus on
and evaluate in the process of choice (and, as shown by Kivetz, Netzer, and Srinivasan [2004],
these effects extend to more complex choice set configurations). That is, options represent the
carriers of value that consumers usually intend to consider when making choices and, as a result,
they often do consider such choice sets in the process of making decisions.
Conversely, unconscious influences are much less likely to operate in the clean form in
which they are typically studied. That is, because such effects in real life are usually unintended
coincidents, they are not protected by task goals and perceived relevance to the choice task. Any
interference by other factors can eliminate or change the direction of such effects. For example,
in the classic study by Bargh, Chen, and Burrows (1996), the presence of one subject who
happened to be in a hurry at the conclusion of the study might have eliminated the effect of prior
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exposure to words related to the elderly on other participants’ walking speed. Because most
consumer choice environments involve multiple potential unconscious influences, the likelihood
that any one effect will operate in the isolated form in which it is demonstrated in the laboratory
is relatively low. Furthermore, although measuring a choice (or other) dependent variable in
close proximity to the manipulation is not unique to research on unconscious influences, it
appears reasonable to assume that priming effects tend to deteriorate more rapidly than, for
example, the effect of the considered options’ attributes. As an aside, it is noteworthy that the
conclusion that “… everything that one encounters is preconsciously screened and classified as
either good or bad, within a fraction of a second after encountering it”1 (Bargh 1997, p. 23; see
also Duckworth et al. 2001) may have limited consequences in many consumer choice situations.
In particular, preconscious classifications, affective reactions (e.g., Slovic et al. 2002), and
confirmation bias notwithstanding, consumers often consciously consider attributes such as
ingredients, features, and specifications, and these factors affect the ultimate choices they make.
Conscious Consideration of Task-Relevant Inputs in Consumer Choice
As Dijksterhuis et al. point out, the typical assumption underlying consumer research, and
psychological research more generally, has been that people consciously process information
before deciding what to buy,” whereas in reality they often do not. Consistent with this
argument, Loewenstein (2001) suggests that “behavioral decision researchers are moving on”
and “are abandoning their own paradigm” (p. 499-500), and he reviews research indicating that
decision making tends to be automatic, habitual, and mindless.
However, as indicated, at this point it appears highly unlikely that the explanatory power
offered by an analysis of unconscious influences will approach that provided by the assumption
that choices are largely determined by conscious processing of task-relevant inputs. This
1
It is noteworthy that such automatic classifications as positive or negative are different from what we typically
refer to as judgments or evaluations. The degree to which initial automatic classification of stimuli as good or bad
determines the valence and intensity of conscious evaluations and the moderators of the relation between automatic
classifications and conscious evaluations still need to be investigated (John Bargh, personal communication).
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conclusion is particularly applicable to typical consumer choices and other choices, where
conscious information processing is supported by both natural focal stimuli (i.e., the choice
options) and by norms regarding the manner in which choices should be made. The conclusion
may be somewhat less applicable to novel and unfamiliar judgment tasks, such as estimating
distances or sizes or determining how much one is willing to pay in order to save birds from
drowning. But the assumption that consumers consciously consider the options available to
them, whether these options fit their preferences, and so on, has been quite effective in allowing
us to predict and explain a wide range of non-obvious marketplace phenomena. Of course, there
is no one paradigm that can account for all choices and the manner in which information
processing generates these choices. For example, whereas the simple assumption of value
maximization can explain many observations, other phenomena appear to be better explained by
portraying decisions as based on the balance of justifications for and against options (e.g., Shafir,
Simonson, and Tversky 1993; Simonson 1989; Slovic 1975).
It must be emphasized again that the “conscious research program” has long abandoned
the naïve assumption that decision makers are aware of the various influences on their
perceptions and behavior. Thus, a typical study of consumer decision making does assume that
information processing of the manipulated stimuli and/or instructions takes place, but
participants are often unaware of the factors driving their responses. The role of conscious
information processing in the following illustrations, most of which are taken from projects in
which I have been involved, is not unlike its apparent role in thousands of other studies.
Although these examples will be only briefly discussed, the assumption of conscious information
processing of task-relevant inputs appears to account for these non-obvious influences on
consumer choice behavior.
Huber, Payne, and Puto (1982) demonstrated the attraction (or asymmetric dominance)
effect, whereby the addition to a two-option set of an option that is inferior relative to one of the
existing options increases the (absolute) choice share of that option. For example, consumers are
more likely to exchange $6 for an elegant Cross pen when they also have the option of
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exchanging $6 for a less attractive pen (Simonson and Tversky 1992). Evidently, the addition of
the asymmetrically inferior option makes the superior option appear more attractive and easier to
justify (e.g., Simonson 1989), even though consumers fail to recognize the impact of the inferior
option on their preferences (e.g., Dhar and Simonson 2003). Thus, the robust asymmetric
dominance effect appears to be driven by a rather detailed processing of the options’ values and
the set configuration, even though consumers tend to misattribute their choices to their tastes.
Kivetz and Simonson (2003) show that idiosyncratic preferences, that is, preferences
perceived to be different from those of most other people, play a key role and often receive
disproportional weight in consumers’ decisions. For example, they demonstrate that students
who liked sushi more than most other students were more likely to join a loyalty program that
offered a reward (movie tickets) for purchasing both 12 sandwiches and 12 orders of sushi than
to join a program that offered the same reward for purchasing just 12 sandwiches. Kivetz and
Simonson referred to the tendency to emphasize idiosyncratic preference match as the
idiosyncratic fit heuristic. Although people are likely to consciously process the provided
information regarding aspects of options that fit their preferences better than most others, they do
not recognize their tendency to emphasize such dimensions. For example, had they been
assigned to a within-subject version of this sushi study, they would have been highly unlikely to
select the dominated program that required more purchases for the same reward.
Liu and Simonson (2004) asked one group of respondents to rank-order a set of rather
similar See’s chocolates. A second group was asked to rate the same chocolate options on a 0 to
100 scale. Next, participants in both groups were given a choice between two dollars and their
favorite chocolate from the set. The results indicated that those who rank-ordered the options
were significantly more likely to select their favorite chocolate over the two dollars. This finding
from a project in progress might reflect the differential preference fluency (Novemsky et al.
2004) and preference separability produced by ranking versus ratings tasks that are applied to a
relatively undifferentiated set of options. Again, although participants could not know what
caused them to behave in a certain way, their choice was based on a conscious evaluation (i.e.,
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ranking or rating) of the options. Finally, successful recent applications of framing by politicians
and marketers have built on conscious processing of salient information as it is presented and on
people’s failure to consider how they would have responded to alternative frames. Such
examples include using the frames, (a) “climate change” rather than “global warming,” (b) “soy
milk” rather than “soy juice,” (c) “death tax” rather than “estate tax,” and (d) “The Patriot Act,”
“The Healthy Forest Initiative,” and “tax relief,” rather than alternative labels.
These examples are similar to numerous other illustrations of choice phenomena that can
be readily explained based on the traditional assumption that choices are determined primarily
by conscious, willful information processing of pertinent, task-relevant inputs, such as various
interpretations of the options’ attributes and their fit with the person’s perceived preferences. As
argued above, because choices naturally focus on options, and people tend to believe that options
need to be evaluated in some fashion before a choice is made, conscious accounts of choice
behavior have an overwhelming advantage over unconscious influences.
The fact that all of the above illustrations involve phenomena that can be characterized as
primarily driven by conscious, willful, controllable evaluation of task-relevant focal inputs does
not mean that decision makers are aware of the processes and the various factors (e.g., primes,
goals, mood) that influence their responses. In that sense, one might argue these phenomena
could also be regarded as unconscious and included under the “99 and 44/100%” of everyday life
that is automatic (Bargh, 1997, p. 243). However, although the literature does not seem to offer
a clear definition of automatic, unconscious influences (though Bargh, 1994, provides a
conceptual classification), the emphasis and potential contribution of that literature go well
beyond the well-established notion that people’s self-insight is limited. Indeed, observations of
limited self-insight and failures of introspection have been well-explained by analyses that have
focused on these issues (e.g., Nisbett and Wilson 1977; Wilson and Schooler 1991).
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Conclusion
The Dijksterhuis et al. article reviews and integrates recent research regarding
unconscious, automatic influences on judgment, decision making, and behavior, and will
promote further research in this emerging, important area of consumer research. In this
commentary, I tried to reflect more broadly on arguments expressed in recent years by several
prominent psychologists and decision researchers that unconscious, automatic influences are the
primary drivers of judgment and choice. It is natural and often important for proponents of
alternative views to highlight the common misconceptions and the under-appreciation of their
conviction.
However, some arguments are too extreme, such as the notion that conscious information
processing of judgment and decision making inputs (e.g., the observed options’ attribute values)
usually just makes sense of behavior after it is executed. Similarly, the notion that unconscious,
automatic processes determine most responses and account for 99 and 44/100% appears
overbroad and does not recognize important distinctions. In particular, the common assumption
that choice is driven primarily by conscious processing of perceived task-relevant inputs still
offers the most parsimonious account of choice behavior. Furthermore, although highly scripted
or habitual responses might be considered non-conscious, they are less interesting and reflect
previously conscious processes.
Thus, it may not be meaningful to characterize judgments, decisions, and behavior as
being normally non-conscious rather than conscious or as System 1 rather than System 2. When
discussing psychological phenomena that are driven mainly by automatic, unconscious
processes, it seems reasonable to refer to consciousness and System 2 processes as overriding the
default and automatic System 1 processes (e.g., Baumeister and Sommer 1997; Kahneman and
Frederick 2002; Sloman 1996). Conversely, when accounting for choices and psychological
phenomena that are driven mainly by task-relevant inputs, processes, and goals (e.g., attributes,
tastes, rules), the characterization of System 2 as occasionally “overriding” System 1 seems less
suitable.
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The intriguing findings regarding unconscious, automatic influences on behavior do
suggest promising directions for future research that incorporates both conscious and
unconscious elements. In particular, using the types of manipulations described by Dijksterhuis
et al., it should be possible to influence the criteria used by consumers (see, e.g., Bettman and
Sujan 1987) and the manner in which options are evaluated. For example, the tendency to
consider regret and counterfactuals, to compromise, and to be in a frugal state of mind may very
well be influenced by unconscious factors, such as imitation and goal pursuit.
Furthermore, reversing the typical order, consciously evaluated choice stimuli might
affect performance in presumably unrelated priming procedures and, in turn, make consumers
more susceptible to predictable priming effects. To illustrate, Dhar and Simonson (1999)
showed that, in tradeoffs between a goal (e.g., pleasure) and a resource (e.g., money) that involve
two choices in the same episode, consumers tend to indicate a preference for “going all the way,”
referred to as highlighting. For example, the same consumer is more likely to take the taxi to the
airport when flying first class and more likely to take the shuttle bus when flying coach.
Conversely, in tradeoffs between two goals such as pleasure and good health, consumers tend to
prefer balancing two components of an episode. For example, most consumers believe that they
would be happier with two meals that balance a tasty but unhealthy appetizer/entrée with a less
tasty but more healthy entrée/appetizer, as opposed to having an all-tasty meal on one occasion
and an all-healthy meal on a second occasion.
Suppose, now, that study participants first consider two related choices and episodes
(e.g., two dinners at a restaurant) involving a tradeoff between pleasure and money (e.g., choices
regarding the taste and cost of both the appetizer and entrée) and indicate a preference for total
pleasure in one episode and low cost in the second episode (i.e., the highlighting rather than the
balancing option). They are then asked to unscramble sentences, solve anagrams, or perform
another seemingly unrelated task. Will participants who chose the highlighting option perform
better in tasks where the solutions are compatible with the highlighted goal? Will such an effect
be stronger for a goal (e.g., pleasure) than for a resource or a constraint (e.g., saving money)?
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More generally, putting aside the question of whether conscious or unconscious,
automatic aspects play a greater role in choice and can better explain consumer decision making,
future research is likely to examine various interactions between conscious information
processing and automatic influences and processes. Such research will promote the convergence
of the two literatures. On the one hand, even the strongest supporters of consciousness recognize
that unconscious, automatic processes can have significant impact on the manner in which
consumers evaluate options and make choices. As the literature on automatic, unconscious
influences further evolves, it is reasonable to expect that researchers will focus less on
demonstrating that such effects exist and turn their attention to interactions between unconscious
and conscious processes. Indeed, non-conscious influences may have their greatest and most
enduring impact when they determine how decision makers consciously think about the objects
of decision.
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